---
description: Measure how helpful an assistant reply is within a dialogue
---

# Dialogue Helpfulness Judge

`DialogueHelpfulnessJudge` inspects the latest assistant reply in the context of preceding turns. It rewards responses that acknowledge the user’s request, use the available context, and offer actionable guidance.

```python title="Scoring a support reply"
from opik.evaluation.metrics import DialogueHelpfulnessJudge

turns = """USER: My VPN disconnects every 5 minutes.\nASSISTANT: Try reinstalling the client.\nUSER: I already did.\n"""

metric = DialogueHelpfulnessJudge()
score = metric.score(
    input=turns,
    output="Can you send logs? I'll escalate to network engineering.",
)

print(score.value)
print(score.reason)
```

## Inputs

| Argument | Type | Required | Description |
| --- | --- | --- | --- |
| `input` | `str` | Optional | Conversation history (alternating USER / ASSISTANT blocks). |
| `conversation` | `list[dict]` | Optional | Structured turns (`{"role": "user", "content": "..."}` | `{"role": "assistant", ...}`). |
| `output` | `str` | **Yes** | Latest assistant reply to score. |

## Configuration

| Parameter | Default | Notes |
| --- | --- | --- |
| `model` | `gpt-5-nano` | Switch to a larger evaluator for complex enterprise workflows. |
| `temperature` | `0.0` | Use low temperature for reproducible benchmarks. |
| `track` | `True` | Record the evaluation in Opik. |
| `project_name` | `None` | Set when routing results to a different project. |

Integrate this judge into regression suites to catch regressions after prompt changes or upgrades to your assistant model.
